We also experiment with the MuJoCo State Pusher environment used by [1]} and [2]}. However, we make two changes, (1) Instead of using a fixed goal position, we use a randomized goal position and learn a goal-conditioned policy and (2) we do not use the sparse environment reward and instead only use the learned rewards for GraphIRL and learning-based baselines. Figure REF  presents our results, we note that GraphIRL achieves slightly lower success rate than the task-specific environment reward (e.g. GraphIRL \(0.455\)  vs Environment Reward \(0.6133\) ). Further, all vision-based baselines perform significantly lower than GraphIRL (e.g. GraphIRL \(0.455\)  vs XIRL \(0.125\)  and TCN \(0.005\) ). For all learning-based methods, we use the data from [1]} as training demonstrations similar to Push experiments conducted in Section .
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